Feature using classification results

Figure 9.3 presents one example of the verification results for the DMI-HIRLAM-U01 research model with a 1 A-km resolution for May 2005 (Mahura et al., 2005 [390]). It shows better prediction of the diurnal cycle of the average wind velocity at 10 m than with the S05-version. On average, the bias for both models was around 1.5 m/s. The verification runs underlined that increasing the resolution (down to 1 km) brings some improvement to the skill of the meteorological forecast. Nevertheless, it will be also very important for further improvements to have more detailed surface features databases and to increase the quality of the land-use classification (LUC) for urban areas. 

In order to reduce the dynamic range and to improve the classification result the square root or the logarithm of x are frequently used. New logarithmic features x. which lie in the intensity range 0 to 100 can be obtained by equation (148) C2503-... 

Modi fi cat ions of the learni ng machine, appropri ate preprocessing, and feature selection improved the classification results. Use of cross terms (whi ch take into account interact i ons between two mass numbers) accelerated the training but had less influence on the predi ctive abilities C1243- The introduction of a width parameter into the learning machine slightly improved the predictive ability and the absolute value of the scalar product could be used as a measure of confidence C3203. 

Table 3 and 4 represent the results for the classification performance based on size, shape and colour based features using LM and BR algorithms, respectively. 

The overall architecture of our proposed system begins with data acquisition of ECG signals, and then the identification of the QRS complex used for the feature extraction procedures. From the QRS waves, coefficients of the polynomial based approach are used as the unique extracted features. By using these coefficients, classification of the features are performed using Multilayer Perceptron Network and finally with this classification results, the identity of unknown attributes can be determined. The proposed model is summarised as in Fig. 1. 

Englehart et al. [19] has conducted a comparison between TD features used by Hudgins [10] and TFD methods. Based on the results of the classification error, WPT was the most effective method. However, he also suggested that there is no clearly superior method between them. Chai et al. [20] had used WT to discriminate between four motions hand grasp, hand extension, forearm supination... 

The classification results obtained using parameter energy feature of quincunx decomposition are presented in Fig. 5... 

Feature vector for classification We used Support Vector Machine (SVM) to trained the feature vectors. The results of the evaluation will be the precision and recall value for each feature. 

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